[WIP][spark] Support timestamp_ntz

[Zouxxyy]

Purpose

Spark support timestamp_ntz since spark3.4, paimon can support it. And there were problems with timestamp between paimon and spark types before, the following is the correct result after modification

Spark Data Type	Paimon Data Type	Atomic Type
TimestampType	LocalZonedTimestamp	true
TimestampNTZType (spark3.4+)	TimestampType	true

Tests

• test("Paimon DDL: create table with timestamp/timestamp_ntz") with parquet, avro, orc. Note: paimon orc timestamp local time zone reader has a bug when change timezone, just skip this.

• Write paimon and parquet, and read it by duckdb:

  
  D select * from 'test_hive.db/paimon_tbl/bucket-0/data-fc7c66db-0126-4709-b44a-3243ba06660e-0.parquet' limit 10;
  ┌───────┬──────────────────────────┬─────────────────────┐
  │  id   │           ts1            │         ts2         │
  │ int32 │ timestamp with time zone │      timestamp      │
  ├───────┼──────────────────────────┼─────────────────────┤
  │     1 │ 2023-01-02 00:00:00+08   │ 2023-01-02 00:00:00 │
  └───────┴──────────────────────────┴─────────────────────┘
  D select * from 'test_hive.db/parquet_tbl/part-00000-d5360ba2-6291-429f-9c0c-57f3ef8bfd82-c000.snappy.parquet' limit 10;
  ┌───────┬──────────────────────────┬─────────────────────┐
  │  id   │           ts1            │         ts2         │
  │ int32 │ timestamp with time zone │      timestamp      │
  ├───────┼──────────────────────────┼─────────────────────┤
  │     1 │ 2023-01-02 00:00:00+08   │ 2023-01-02 00:00:00 │
  └───────┴──────────────────────────┴─────────────────────┘

D select name, type, converted_type, logical_type from parquet_schema('test_hive.db/paimon_tbl/bucket-0/data-fc7c66db-0126-4709-b44a-3243ba06660e-0.parquet'); ┌───────────────┬─────────┬──────────────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ name │ type │ converted_type │ logical_type │ │ varchar │ varchar │ varchar │ varchar │ ├───────────────┼─────────┼──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ paimon_schema │ │ │ │ │ id │ INT32 │ │ │ │ ts1 │ INT64 │ TIMESTAMP_MICROS │ TimestampType(isAdjustedToUTC=1, unit=TimeUnit(MILLIS=, MICROS=MicroSeconds(), NANOS=)) │ │ ts2 │ INT64 │ TIMESTAMP_MICROS │ TimestampType(isAdjustedToUTC=0, unit=TimeUnit(MILLIS=, MICROS=MicroSeconds(), NANOS=)) │ └───────────────┴─────────┴──────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────┘ D select name, type, converted_type, logical_type from parquet_schema('test_hive.db/parquet_tbl/part-00000-d5360ba2-6291-429f-9c0c-57f3ef8bfd82-c000.snappy.parquet'); ┌──────────────┬─────────┬──────────────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ name │ type │ converted_type │ logical_type │ │ varchar │ varchar │ varchar │ varchar │ ├──────────────┼─────────┼──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ spark_schema │ │ │ │ │ id │ INT32 │ │ │ │ ts1 │ INT64 │ TIMESTAMP_MICROS │ TimestampType(isAdjustedToUTC=1, unit=TimeUnit(MILLIS=, MICROS=MicroSeconds(), NANOS=)) │ │ ts2 │ INT64 │ TIMESTAMP_MICROS │ TimestampType(isAdjustedToUTC=0, unit=TimeUnit(MILLIS=, MICROS=MicroSeconds(), NANOS=)) │ └──────────────┴─────────┴──────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────┘

write paimon orc and read with orc-utils

orc-tools meta data-84a39541-1165-47f9-adba-478e193a469e-0.orc Structure for data-84a39541-1165-47f9-adba-478e193a469e-0.orc File Version: 0.12 with ORC_14 by ORC Java 1.9.2 Rows: 1 Compression: ZSTD Compression size: 262144 Calendar: Julian/Gregorian Type: struct<id:int,binary:binary,ts:timestamp with local time zone,ts_ntz:timestamp>

### Compatibility (WARNING!!)

For the previous written paimon table, since its paimon schema is `TimestampType`, spark will interprete it as `TimestampNTZType` and read it correctly (I had copy `TimestampNTZType` to  paimon-spark3.2 and paimon-spark3.3).
I tested read old paimon ts table manually, there is no compatibility issue and can be read and write correctly, for god sake...

For the new table after this PR, everything goes well, timestamp will be correctly parsed as `TimestampType` while timestamp_ntz wil be parsed as `TimestampNTZType`.

### API and Format

<!-- Does this change affect API or storage format -->

### Documentation